Beamforming in MIMO broadcast relay networks with multiple antenna users
Bibliographic record
Abstract
This paper studies a multiple-input multiple-output (MIMO) broadcast relay channel (BRC) in which a multiple-antenna base station (BS) communicates with multiple-antenna users through a fixed infrastructure-based multiple-antenna relay station (RS). Applying dirty paper coding (DPC) at the BS and linear processing at the RS, our goal is to find the optimal input covariance matrices at the BS and the beamforming matrix at the RS that jointly maximize the system sum-rate. To solve the optimization problem, a more tractable dual multiple access relay channel (MARC) is investigated and an iterative algorithm is proposed to obtain the optimal matrices for the dual system. The mapping from the resulting covariance matrices for the MARC to the covariance matrices for the original BRC is derived. Unlike other existing MIMO BRC schemes, designed for networks with single-antenna users only, our solution is applicable to networks with multiple-antenna users. Compared with two such single-antenna-user schemes, simulations show that the proposed scheme outperforms the all-pass relay design and performs similar to the SVD-relay design. Also, the proposed design performs close to a sum-rate upper bound with the gap decreasing with increasing number of users' antennas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".